Paper Info
Title
Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels.
Abstract
We consider regularized risk minimization in a large dictionary of Reproducing kernel Hilbert Spaces (RKHSs) over which the target function has a sparse representation. This setting, commonly referred to as Sparse Multiple Kernel Learning (MKL), may be viewed as the non-parametric extension of group sparsity in linear models. While the two dominant algorithmic strands of sparse learning, namely convex relaxations using l1 norm (e.g., Lasso) and greedy methods (e.g., OMP), have both been rigorously extended for group sparsity, the sparse MKL literature has so farmainly adopted the former withmild empirical success. In this paper, we close this gap by proposing a Group-OMP based framework for sparse multiple kernel learning. Unlike l1-MKL, our approach decouples the sparsity regularizer (via a direct l0 constraint) from the smoothness regularizer (via RKHS norms) which leads to better empirical performance as well as a simpler optimization procedure that only requires a black-box single-kernel solver. The algorithmic development and empirical studies are complemented by theoretical analyses in terms of Rademacher generalization bounds and sparse recovery conditions analogous to those for OMP [27] and Group-OMP [16].
Year
Venue
Field
2011
NIPS
Kernel (linear algebra),Matching pursuit,Hilbert space,Mathematical optimization,K-SVD,Computer science,Sparse approximation,Lasso (statistics),Multiple kernel learning,Artificial intelligence,Reproducing kernel Hilbert space,Machine learning
DocType
Citations 
PageRank 
Conference
8
0.62
References 
Authors
19
2
Name
Order
Citations
PageRank
Vikas Sindhwani13423154.85
Aurelie C. Lozano214520.21